Long-lead flood forecasting for India: challenges, opportunities, outline Tom Hopson.
Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric...
-
Upload
logan-patterson -
Category
Documents
-
view
219 -
download
3
Transcript of Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric...
![Page 1: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/1.jpg)
Technological Improvements in Flood ForecastingTechnological Improvements in Flood Forecasting
Thomas HopsonThomas HopsonNational Center for Atmospheric Research (NCAR)National Center for Atmospheric Research (NCAR)
![Page 2: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/2.jpg)
Overview:Technological improvements in flood forecasting
I. New data sets for flood forecasting- satellite-derived precipitation estimates- ensemble weather forecasts
II. Coupling new data sets to hydrological models- case study: Bangladesh CFAB project
III. Future improvements: remotely-sensed river discharge- Dartmouth Flood Observatory
IV. Future improvements: catchment-scale water balance- GRACE satellite system
![Page 3: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/3.jpg)
Satellite-derived Rainfall Estimates
1) Satellite-derived estimates: NASA TRMM (GPCP)0.25º X 0.25º spatial resolution; 3hr temporal resolution6hr reporting delaygeostationary infrared “cold cloud top” estimates calibrated from SSM/I and TMI microwave instruments
2) Satellite-derived estimates: NOAA CPC “CMORPH”0.25º X 0.25º spatial resolution; 3hr temporal resolution18hr reporting delay precipitation rain rates derived from microwave instruments (SSM/I, TMI, AMSU-B), but “cloud tracking” done using infrared satellites
Both centers now producing rapid 8km X 8km spatial resolution; 30min temporal resolution; 3hr latency (roughly)
Other similar products: NRL, CSU, PERSIANN
3) Rain gauge estimates: NOAA CPC and WMO GTS0.5º X 0.5º spatial resolution; 24h temporal resolution24hr reporting delay
![Page 4: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/4.jpg)
Spatial Comparison of Precipitation Products
Monsoon season (Aug 1, 2004)Indian subcontinent
TRMM
![Page 5: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/5.jpg)
Weather Forecasts for Hydrologic ApplicationsECMWF example
• Seasonal -- ECMWF System 3- based on: 1) long predictability of ocean circulation, 2) variability in tropical
SSTs impacts global atmospheric circulation- coupled atmosphere-ocean model integrations- out to 7 month lead-times, integrated 1Xmonth
- 41 member ensembles, 1.125º X 1.125º (TL159L62), 130km• Monthly forecasts -- ECMWF
- “fills in the gaps” -- atmosphere retains some memory with ocean variability impacting atmospheric circulation
- coupled ocean-atmospheric modeling after 10 days- 15 to 32 day lead-times, integrated 1Xweek
- 51 member ensemble, 1.125º X 1.125º (TL159L62), 130km• Medium-range -- ECMWF EPS
- atmospheric initial value problem, SST’s persisted- 6hr - 15 day lead-time forecasts, integrated 2Xdaily
- 51 member ensembles, 0.5º X 0.5º (TL255L40), 80km
Motivation for generating ensemble forecasts (weather or hydrologic): a well-calibrated ensemble forecast provides a prognosis of its own uncertainty
or level of confidence
![Page 6: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/6.jpg)
-- Weather forecast skill (RMS error) increases with spatial (and temporal) scale
=> Utility of weather forecasts in flood forecasting increases for larger catchments
-- Logarithmic increase
Rule of Thumb:
![Page 7: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/7.jpg)
Overview:Technological improvements in flood forecasting
I. New data sets for flood forecasting- satellite-derived precipitation estimates- ensemble weather forecasts
II. Coupling new data sets to hydrological models- case study: Bangladesh CFAB project
III. Future improvements: remotely-sensed river discharge- Dartmouth Flood Observatory
IV. Future improvements: catchment-scale water balance- GRACE satellite system
![Page 8: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/8.jpg)
CFAB Project: Improve Bangladesh flood warning lead time
Problems:1. Limited warning of upstream river discharges2. Precipitation forecasting in tropics difficult
Assets:1. Good data inputs=> ECMWF weather forecasts, satellite rainfall estimates2. Large catchments => weather forecasting skill “integrates” over large spatial and temporal scales3. Partnership with Bangladesh’s Flood Forecasting Warning Centre (FFWC)=> daily border river readings used in data assimilation scheme
Technical: Peter Webster (PI), GTA.R. Subbiah, ADPCFunding: USAID, CARE, ECMWF
![Page 9: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/9.jpg)
![Page 10: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/10.jpg)
Merged FFWC-CFAB Hydraulic Model Schematic
Primary forecast boundary conditions shown in gold:
Ganges at Hardinge Bridge
Brahmaputra at Bahadurabad
Benefit: FFWC daily river discharge observations used in forecast data assimilation scheme (Auto-Regressive Integrated Moving Average model [ARIMA] approach)
![Page 11: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/11.jpg)
Daily Operational Flood Forecasting Sequence
Forecast Trigger: ECMWF forecast files
Updated TRMM-CMORPH-CPC precipitation estimates
Updated distributed model parameters
Updated outlet discharge estimates
Above-critical-level forecast probabilities transferred to Bangladesh
Lumped Model Hindcast/Forecast Discharge Generation
Distributed Model Hindcast/Forecast Discharge Generation
Multi-Model Hindcast/Forecast Discharge Generation
Discharge Forecast PDF Generation
Calibrate model
Statistically corrected downscaled forecasts
Generate forecasts Generate hindcasts Generate forecasts Generate hindcasts
Update soil moisture states and in-stream flows
Generate hindcasts
Calibrate AR error model
Calibrate multi-model
Generate forecasts Generate hindcasts
Generate forecasted model error PDF
Convolve multi-model forecast PDF with model error PDF
Generate forecasts
![Page 12: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/12.jpg)
Weather Forecast Ensembles Transformed into Discharge Forecasts Ensembles
3 day 4 day
Precipitation Forecasts
1 day 4 day
7 day 10 day
1 day 4 day
7 day 10 day
Discharge Forecasts
![Page 13: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/13.jpg)
Transforming (Ensemble) Rainfall into Transforming (Ensemble) Rainfall into (Probabilistic) River Flow Forecasts(Probabilistic) River Flow Forecasts
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0 1 2 3 4 5 6
Rainfall Probability
Rainfall [mm]
Discharge Probability
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
10,000 30,000 50,000 70,000 90,000
Discharge [m3/s]
Above danger level probability 36%Greater than climatological seasonal risk?
![Page 14: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/14.jpg)
Daily Operational Flood Forecasting Sequence
Forecast Trigger: ECMWF forecast files
Updated TRMM-CMORPH-CPC precipitation estimates
Updated distributed model parameters
Updated outlet discharge estimates
Above-critical-level forecast probabilities transferred to Bangladesh
Lumped Model Hindcast/Forecast Discharge Generation
Distributed Model Hindcast/Forecast Discharge Generation
Multi-Model Hindcast/Forecast Discharge Generation
Discharge Forecast PDF Generation
Calibrate model
Statistically corrected downscaled forecasts
Generate forecasts Generate hindcasts Generate forecasts Generate hindcasts
Update soil moisture states and in-stream flows
Generate hindcasts
Calibrate AR error model
Calibrate multi-model
Generate forecasts Generate hindcasts
Generate forecasted model error PDF
Convolve multi-model forecast PDF with model error PDF
Generate forecasts
![Page 15: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/15.jpg)
2003 Model Comparisons for the Ganges (4-day lead-time)
hydrologic distributed modelhydrologic lumped model
Resultant Hydrologic multi-modelMulti-Model-Ensemble Approach:• Rank models based on historic residual error using current model calibration and “observed” precipitation
•Regress models’ historic discharges to minimize historic residuals with observed discharge
•To avoid over-calibration, evaluate resultant residuals using Akaike Information Criteria (AIC)
•If AIC minimized, use regression coefficients to generate “multi-model” forecast; otherwise use highest-ranked model => “win-win” situation!
![Page 16: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/16.jpg)
Multi-Model Forecast Weighting Multi-Model Forecast Weighting (Regression) Coefficients(Regression) Coefficients
- Lumped model (red)- Lumped model (red)
- Distributed model (blue)- Distributed model (blue)
Significant catchment variation
Coefficients vary with the forecast lead-time
Representative of the each basin’s hydrology
Ganges slower time-scale response Brahmaputra “flashier”
Improvements: incorporating 78 multi-Improvements: incorporating 78 multi-model approach (M. Clark, NIWA)model approach (M. Clark, NIWA)
- blending elements from ARNO/VIC, - blending elements from ARNO/VIC,
PRMS, Sacramento, TOPmodelPRMS, Sacramento, TOPmodel
![Page 17: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/17.jpg)
Daily Operational Flood Forecasting Sequence
Forecast Trigger: ECMWF forecast files
Updated TRMM-CMORPH-CPC precipitation estimates
Updated distributed model parameters
Updated outlet discharge estimates
Above-critical-level forecast probabilities transferred to Bangladesh
Lumped Model Hindcast/Forecast Discharge Generation
Distributed Model Hindcast/Forecast Discharge Generation
Multi-Model Hindcast/Forecast Discharge Generation
Discharge Forecast PDF Generation
Calibrate model
Statistically corrected downscaled forecasts
Generate forecasts Generate hindcasts Generate forecasts Generate hindcasts
Update soil moisture states and in-stream flows
Generate hindcasts
Calibrate AR error model
Calibrate multi-model
Generate forecasts Generate hindcasts
Generate forecasted model error PDF
Convolve multi-model forecast PDF with model error PDF
Generate forecasts
![Page 18: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/18.jpg)
Final flood forecast “calibration” or “post-processing”
Pro
babi
lity
calibration
Flow rate [m3/s]
Pro
babi
lity
Post-processing has corrected:• the “on average” bias• as well as under-representation of the 2nd moment of the empirical forecast PDF (i.e. corrected its “dispersion” or “spread”)
“spread” or “dispersion”
“bias”obs
obs
ForecastPDF
ForecastPDF
Flow rate [m3/s]
Our approach:• under-utilized “quantile regression” approach• probability distribution function “means what it says”• daily variation in the ensemble dispersion directly relate to changes in forecast skill
![Page 19: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/19.jpg)
2004 Brahmaputra Ensemble Forecasts and Danger Level Probabilities
3 day 4 day
5 day
3 day 4 day
5 day
7 day 8 day
9 day 10 day
7-10 day Ensemble Forecasts
7 day 8 day
9 day 10 day
7-10 day Danger Levels
![Page 20: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/20.jpg)
Five Pilot Sites chosen in 2006 consultation workshops based on biophysical, social criteria:
Rajpur Union -- 16 sq km-- 16,000 pop.
Uria Union-- 23 sq km-- 14,000 pop.
Kaijuri Union-- 45 sq km-- 53,000 pop.
Gazirtek Union-- 32 sq km-- 23,000 pop.
Bhekra Union-- 11 sq km-- 9,000 pop.
A v e r a g e D a m a g e ( T k . ) p e r H o u s e h o l d i n P i l o t U n i o n
7 , 2 5 5
2 8 , 7 4 5
6 0 , 9 9 3
6 4 , 0 0 0
4 0 5 8
0
1 0 , 0 0 0
2 0 , 0 0 0
3 0 , 0 0 0
4 0 , 0 0 0
5 0 , 0 0 0
6 0 , 0 0 0
7 0 , 0 0 0
U r i a G a z i r t e k K a i j u r i R a j p u r B e k r a
U n i o n
Average Damage (Tk) per
Household
![Page 21: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/21.jpg)
2007 Brahmaputra Ensemble Forecasts and Danger Level Probabilities
7-10 day Ensemble Forecasts 7-10 day Danger Levels
7 day 8 day
9 day 10 day
7 day 8 day
9 day 10 day
![Page 22: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/22.jpg)
Overview:Technological improvements in flood forecasting
I. New data sets for flood forecasting- satellite-derived precipitation estimates- ensemble weather forecasts
II. Coupling new data sets to hydrological models- case study: Bangladesh CFAB project
III. Future improvements: remotely-sensed river discharge- Dartmouth Flood Observatory
IV. Future improvements: catchment-scale water balance- GRACE satellite system
![Page 23: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/23.jpg)
Satellite-based River Discharge EstimationBob Brakenridge, Dartmouth Flood Observatory, Dartmouth College
2000
2200
2400
2600
2800
1-Jan-056-Jan-0511-Jan-0516-Jan-0521-Jan-0526-Jan-0531-Jan-055-Feb-05
10-Feb-0515-Feb-0520-Feb-0525-Feb-052-Mar-057-Mar-05
12-Mar-0517-Mar-0522-Mar-0527-Mar-051-Apr-056-Apr-05
11-Apr-0516-Apr-0521-Apr-0526-Apr-051-May-056-May-0511-May-0516-May-0521-May-0526-May-0531-May-05
5-Jun-0510-Jun-0515-Jun-0520-Jun-0525-Jun-0530-Jun-05
5-Jul-0510-Jul-0515-Jul-0520-Jul-0525-Jul-0530-Jul-054-Aug-059-Aug-05
14-Aug-0519-Aug-0524-Aug-0529-Aug-053-Sep-058-Sep-05
T, degrees K x 10010000200003000040000500006000070000
Discharge, c.f.s.
Measurement Reach Calibration Target Estimated Discharge Measured Discharge at Piketon
River Watch
![Page 24: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/24.jpg)
Application to the Ganges and Brahmaputra Rivers
QuickTime™ and aTIFF (Uncompressed) decompressor
are needed to see this picture.
Utility of River Watch discharge estimates to flood forecasting:1) Calibration of ungauged subcatchments outflow and routing2) Operational improvements through data assimilation
-- blending of enKF, 4DVAR, and “quantile regression”
Ganges River Watch sitesBrahmaputra floodwave isochrons
![Page 25: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/25.jpg)
Overview:Technological improvements in flood forecasting
I. New data sets for flood forecasting- satellite-derived precipitation estimates- ensemble weather forecasts
II. Coupling new data sets to hydrological models- case study: Bangladesh CFAB project
III. Future improvements: remotely-sensed river discharge- Dartmouth Flood Observatory
IV. Future improvements: catchment-scale water balance- GRACE satellite system
![Page 26: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/26.jpg)
Gravity Recovery And Climate Experiment (GRACE)
Slide from Sean Swenson, NCAR
![Page 27: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/27.jpg)
GRACE catchment-integrated soil moisture estimates useful for:GRACE catchment-integrated soil moisture estimates useful for:
1) Hydrologic model calibration and validation1) Hydrologic model calibration and validation
2) Seasonal forecasting2) Seasonal forecasting
3) Data assimilation for medium-range (1-2 week) forecasts3) Data assimilation for medium-range (1-2 week) forecasts
Slide from Sean Swenson, NCAR
![Page 28: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/28.jpg)
ConclusionsConclusionsExciting time for flood forecasting for both developed and developing countries:Exciting time for flood forecasting for both developed and developing countries:-- satellite-based observational sensors provide global and timely estimates of water budget -- satellite-based observational sensors provide global and timely estimates of water budget componentscomponents-- coupling hydrologic forecast models to (ensemble) weather forecasts greatly extends -- coupling hydrologic forecast models to (ensemble) weather forecasts greatly extends forecast time-horizonforecast time-horizon
Case study: CFAB Brahmaputra and Ganges river flow forecasts:Case study: CFAB Brahmaputra and Ganges river flow forecasts:-- 2003: went operational with ECMWF ensemble weather forecasts-- 2003: went operational with ECMWF ensemble weather forecasts-- 2004: 1) forecasts fully-automated; 2) forecasted severe Brahmaputra July flooding events-- 2004: 1) forecasts fully-automated; 2) forecasted severe Brahmaputra July flooding events-- 2007: 5 pilot areas warned citizens many days in-advance during two (July-August, -- 2007: 5 pilot areas warned citizens many days in-advance during two (July-August, September) severe Brahmaputra flooding eventsSeptember) severe Brahmaputra flooding events
Further Advances:Further Advances:Data assimilation of new satellite-derived products:Data assimilation of new satellite-derived products:-- Dartmouth Flood Observatory river discharge estimates-- Dartmouth Flood Observatory river discharge estimates-- GRACE integrated catchment soil moisture-- GRACE integrated catchment soil moisture-- QSCAT and TMI soil moisture estimates (Nghiem, JPL)-- QSCAT and TMI soil moisture estimates (Nghiem, JPL)
Expansion of multi-model approach (78 member multi-model)Expansion of multi-model approach (78 member multi-model)
Daily-updated seamless weather-to-seasonal flood forecasting:Daily-updated seamless weather-to-seasonal flood forecasting:-- utilizing short-, medium-, monthly-, and seasonal ensemble forecasts-- utilizing short-, medium-, monthly-, and seasonal ensemble forecasts
![Page 29: Technological Improvements in Flood Forecasting Thomas Hopson National Center for Atmospheric Research (NCAR)](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697c0111a28abf838ccba59/html5/thumbnails/29.jpg)
Thank [email protected]